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BACKGROUND: Succinate dehydrogenase (SDH) has been identified as one of the most significant targets for fungicide discovery. To date, 23 commercial SDH inhibitor (SDHI) fungicides have been approved for plant protection since the first launch of carboxin in 1966, and extensively applied to combat destructive plant fungi. RESULTS:In this project, 20 novel pyridine sulfide derivatives containing SDH-based heterocyclic amide fungicide were designed, synthesized, and characterized by proton nuclear magnetic resonance ( 1 H-NMR), carbon-13 ( 13 C)-NMR and highresolution mass spectrometry (HRMS). In vitro fungicidal activity experiment, the target compound I-1 displayed excellent inhibitory rates against the common agricultural pathogens with half maximal effective concentration (EC 50 ) values of 5.2 to 39.8 ∼g mL −1 . The in vivo fungicidal activities demonstrated that the compound I-1 could effectively prevent Botrytis cinerea from infecting tomato and cucumber leaves with the preventative rates of 67% and 50%. The mitochondrial membrane potential detection, SDH enzyme assay and the molecular docking simulation revealed that the mechanism of action of the compound I-1 and the relevant interactions with the target enzyme may be similar to those of the control fluopyram.CONCLUSION: The biological activity screening and validation of mechanism of action indicated that the compound I-1 could be identified as a potential SDH inhibitor for further study.

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm. 1 R. Fan, J. Jiao and M. Liu are with the Robotics and Multi-Perception Laboratory in Robotics Institute at